Neural networks for advanced control of robot manipulators

  • Patino H
  • Carelli R
  • Kuchen B
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Abstract

This paper presents an approach and a systematic

design methodology to adaptive motion control based on neural

networks (NNs) for high-performance robot manipulators, for

which stability conditions and performance evaluation are given.

The neurocontroller includes a linear combination of a set of

off-line trained NNs (bank of fixed neural networks), and an

update law of the linear combination coefficients to adjust robot

dynamics and payload uncertain parameters. A procedure is presented

to select the learning conditions for each NN in the bank.

The proposed scheme, based on fixed NNs, is computationally

more efficient than the case of using the learning capabilities

of the neural network to be adapted, as that used in feedback

architectures that need to propagate back control errors through

the model (or network model) to adjust the neurocontroller. A

practical stability result for the neurocontrol system is given. That

is, we prove that the control error converges asymptotically to a

neighborhood of zero, whose size is evaluated and depends on the

approximation error of the NN bank and the design parameters

of the controller. In addition, a robust adaptive controller to NN

learning errors is proposed, using a sign or saturation switching

function in the control law, which leads to global asymptotic

stability and zero convergence of control errors. Simulation

results showing the practical feasibility and performance of the

proposed approach to robotics are given.

Index Terms�Adaptive control, feedforward neural n

Author-supplied keywords

  • Adaptive control; feedforward neural nets; neuroco

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Authors

  • H D Patino

  • R Carelli

  • B R Kuchen

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